바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

ISSN : 1013-0799

Article Contents

Prev
e-Submission

Vol.39 No.3
Citation Share

Identification of User Preference Factor Using Review Information

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2022, v.39 no.3, pp.311-336
https://doi.org/10.3743/KOSIM.2022.39.3.311
Sungjeon Song
Jiyoung Shim (Yonsei University)
Song, S., & Shim, J. (2022). Identification of User Preference Factor Using Review Information. Journal of the Korean Society for Information Management, 39(3), 311-336, https://doi.org/10.3743/KOSIM.2022.39.3.311
  • Downloaded
  • Viewed
PDF Download

Abstract

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users’ book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories(‘Content’, ‘Character’, ‘Writing’, ‘Reading’, ‘Author’, ‘Story’, ‘Form’) were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

keywords
user preference, review data, natural language processing, topic modeling, content analysis
Submission Date
2022-08-21
Revised Date
2022-09-04
Accepted Date
2022-09-08

Journal of the Korean Society for Information Management

e-Submission OA Policy